Determination of Fatigue Following Maximal Loaded Treadmill Exercise by Using Wavelet Packet Transform Analysis and MLPNN from MMG-EMG Data Combinations

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• 作者：Gürkan Bilgin ; ?. Ethem Hindistan ; Y. Gül ?zkaya…
• 关键词：Muscle fatigue ; EMG ; MMG ; Bruce protocol ; Wavelet packets ; MLPNN
• 刊名：Journal of Medical Systems
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：39
• 期：10
• 全文大小：1,436 KB
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• 作者单位：Gürkan Bilgin (1) (5)
  ?. Ethem Hindistan (2)
  Y. Gül ?zkaya (2)
  Etem K?klükaya (3)
  ?vün? Polat (4)
  ?mer H. ?olak (4)
  
  1. Vocational School of Technical Sciences, Mehmet Akif Ersoy University, Burdur, Turkey
  5. Institute of Natural Science, Department of Electrical and Electronics Engineering, Sakarya University, Sakarya, Turkey
  2. School of Physical Education and Sports, Akdeniz University, Antalya, Turkey
  3. Faculty of Engineering, Department of Electrical and Electronics Engineering, Gazi University, Ankara, Turkey
  4. Faculty of Engineering, Department of Electrical and Electronics Engineering, Akdeniz University, Antalya, Turkey
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics for Life Sciences, Medicine and Health Sciences
  Health Informatics and Administration
  
• 出版者：Springer Netherlands
• ISSN：1573-689X

文摘

The muscle fatigue can be expressed as decrease in maximal voluntary force generating capacity of the neuromuscular system as a result of peripheral changes at the level of the muscle, and also failure of the central nervous system to drive the motoneurons adequately. In this study, a muscle fatigue detection method based on frequency spectrum of electromyogram (EMG) and mechanomyogram (MMG) has been presented. The EMG and MMG data were obtained from 31 healthy, recreationally active men at the onset, and following exercise. All participants were performed a maximally exercise session in a motor-driven treadmill by using standard Bruce protocol which is the most widely used test to predict functional capacity. The method used in the present study consists of pre-processing, determination of the energy value based on wavelet packet transform, and classification phases. The results of the study demonstrated that changes in the MMG 176-34?Hz and EMG 254-13?Hz bands are critical to determine for muscle fatigue occurred following maximally exercise session. In conclusion, our study revealed that an algorithm with EMG and MMG combination based on frequency spectrum is more effective for the detection of muscle fatigue than EMG or MMG alone.